Over the past 50 years, a demographic shift has occurred in the workforce. The Bureau of Labor Statistics (BLS) projects that the labor force participation rate of age 55 and older will continue to exhibit strong growth, reaching more than 42 percent in 2016 [1]. With a confluence of an aging workforce and unknown risk factors, a rise in age-related degenerative diseases will dramatically impact workforce productivity and the economy. As such, pursuing the etiology of costly neurodegenerative diseases like amyotrophic lateral sclerosis (ALS) is imperative for public health. A consistent association exists between electric occupations and ALS; however, it is unclear what aspect of the job might be responsible. Electric shocks and magnetic-field (EMF) exposures have been proposed as potential culprits. Compounding the problem is the current knowledge gaps about which specific occupations are experiencing electric shocks. The main aims of this proposal are to 1) identify workers exposed to electric shocks using multiple data sources and expert judgment and incorporate these data into an existing magnetic field job exposure matrix (JEM), 2) conduct a case-control study using the newly expanded JEM and U.S. mortality data, and 3) quantify potential sources of methodologic biases and identify the largest sources of uncertainty. In Specific Aim #1, we propose to develop a comprehensive, qualitative electric shocks and magnetic fields job exposure matrix (JEM) able to characterize many occupations at risk. We will achieve this aim by using numerous best available U.S. data sources on workplace electric shocks and expert judgment, quantifying uncertainty in the process. Our approach will result in a JEM and enhanced by expert judgments to be incorporated as priors into an occupational multiple-bias analysis. This JEM may be used in other studies with complete work histories. With new detailed information, we will pursue Specific Aim #2: to examine and disentangle the relationship between occupational MF, electric shocks and ALS. While previous studies have been published on occupational MF and ALS, this study will be novel in its attempt to simultaneously address electric shocks. Given its potential contribution to informing the EMF risk assessment, it is important to estimate uncertainties stemming from epidemiologic design and model assumptions. In Specific Aim #3, we propose to quantify biases from confounding, selection and exposure misclassification, so that proper inferences may be drawn from the case-control analysis of mortality data. Unlike traditional sensitivity analysis, we will employ multiple bias analysis which corrects for biases simultaneously. Integration of better exposure assessment methods and novel epidemiologic analyses will aid in framing the role occupational EMFs and electric shocks play in the etiology of ALS.